Business process variant analysis based on mutual fingerprints of event logs

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.This research is partly funded by the Australian Research Council (DP180102839) and Spanish funds MINECO and FEDER (TIN2017-86727-C2-1-R).Peer ReviewedPostprint (author's final draft

Tendências do BPM

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de InformaçãoAtualmente, as organizações encontram-se inseridas em ambientes de mercado cada vez mais competitivos, deparando-se com várias dificuldades, em que face a estas, necessitam de encontrar soluções. Por essa razão, viram o BPM como uma solução para melhorar o seu negócio. Um dos objetivos do BPM é ter a capacidade de identificar, monitorar e otimizar processos de negócio cujo resultado final é um conjunto de atividades realizadas. Com base nesta monitorização e otimização, as organizações tornam-se capazes de identificar possíveis lacunas nos seus processos e com isto melhorá-los. Com isto, verificou-se a falta de informação existente cientificamente em relação à identificação de novas tendências para o BPM. Neste sentido, com este trabalho propomos realizar uma investigação seguindo a metodologia de pesquisa em Design Science Research, em que iniciamos uma pesquisa de levantamento de tendência seguindo a abordagem proposta por Webster e Watson (2002), com base em duas conferências internacionais em BPM de ranking elevado, em que se identificou os tópicos mais abordados como também problemas e soluções desde 2013 até 2015. Posteriormente, com informação recolhida ao longo de três anos, através da criação de um framework identificamos algumas tendências para o BPM, de forma a melhorá-lo. Para garantir a credibilidade dos resultados, através da criação de um inquérito por questionário realizou-se a avaliação dos resultados obtidos.Nowadays, the market gets more and more competitive, thus companies need to learn how to manage and find the right solutions for their business when facing challenges. For that reason, they saw BPM as a great tool to expand their business. One of the features of BPM is the capacity to identify, monetize and optimize processes within the business which ultimately allow for an aggregation of performed activities. Thanks to these features, the business have been capable of identifying possible gaps in their processes and how to improve them. With this, it was verified the lack of scientific information regarding the identification of new trends for BPM. Therefore, with this work we propose to conduct an investigation that follows the searching methodology in Design Science Research, where we initiate a search of lifting trends as proposed by Webster and Watson (2002). This is based on two international conferences on BPM, in which it identified the most discussed topics and also the problems and solutions since 2013 until 2015. After this investigation, with collected information over 3 years, through the creation of framework we identify some BPM trends. To approve this results, we created a survey that was held an evaluation of the final results

Navigating in Complex Process Model Collections

The increasing adoption of process-aware information systems (PAIS) has led to the emergence of large process model collections. In the automotive and healthcare domains, for example, such collections may comprise hundreds or thousands of process models, each consisting of numerous process elements (e.g., process tasks or data objects). In existing modeling environments, process models are presented to users in a rather static manner; i.e., as image maps not allowing for any context-specific user interactions. As process participants have different needs and thus require specific presentations of available process information, such static approaches are usually not sufficient to assist them in their daily work. For example, a business manager only requires an abstract overview of a process model collection, whereas a knowledge worker (e.g., a requirements engineer) needs detailed information on specific process tasks. In general, a more flexible navigation and visualization approach is needed, which allows process participants to flexibly interact with process model collections in order to navigate from a standard (i.e., default) visualization of a process model collection to a context-specific one. With the Process Navigation and Visualization (ProNaVis) framework, this thesis provides such a flexible navigation approach for large and complex process model collections. Specifically, ProNaVis enables the flexible navigation within process model collections along three navigation dimensions. First, the geographic dimension allows zooming in and out of the process models. Second, the semantic dimension may be utilized to increase or decrease the level of detail. Third, the view dimension allows switching between different visualizations. All three navigation dimensions have been addressed in an isolated fashion in existing navigation approaches so far, but only ProNaVis provides an integrated support for all three dimensions. The concepts developed in this thesis were validated using various methods. First, they were implemented in the process navigation tool Compass, which has been used by several departments of an automotive OEM (Original Equipment Manufacturer). Second, ProNaVis concepts were evaluated in two experiments, investigating both navigation and visualization aspects. Third, the developed concepts were successfully applied to process-oriented information logistics (POIL). Experimental as well as empirical results have provided evidence that ProNaVis will enable a much more flexible navigation in process model repositories compared to existing approaches

Analysing the Impact of Changes in User Interface of e-Health Record Systems on Clinical Pathways using Process Mining

The provision of care in a hospital includes a series of activities that are often recorded in the electronic health record (EHR) systems. Analysing the data in these EHRs has the potential to support the understanding of care processes and exploring the opportunities for process improvement. One of the emerging data analytics approaches for such analyses is process mining, and one critical challenge in working with EHR data is that processes might change over time. This thesis uses a process mining approach to detect process change over time and analyse the impact of those changes on the EHR data. The overall aim is to summarise the attributable change in the data due to the process so that clinicians can better analyse the data. Three datasets were used in this study to understand the variability of the EHR systems. The first dataset is a publicly available EHR data that was used for developing the methods and supporting the reproducibility of the research. The second dataset is a de-identified subset of the database of cancer patients from the Leeds Cancer Centre. The second dataset was used in the experiments to improve on the results of a previous study using the same dataset. The third dataset was the full Leeds Cancer Centre EHR database after more comprehensive ethics was approved. In the third dataset, experiments were done to analyse the impact of a known system change on clinical pathways and to explore process change over time without a known system change. All three datasets were analysed using process mining. Process mining was shown to be useful for analysing clinical pathways and exploring process changes over time. It can be used to visualise the process before and after a known change. When the system change is unknown, process mining can be used to explore the process execution over time and identify the potential period where the system was changed. This thesis explores some aspects of the complex interrelatedness of process and user interface (UI) of the EHR system